scholarly journals Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study

2021 ◽  
Vol Volume 13 ◽  
pp. 1449-1459
Author(s):  
Jean-Benoit Martinot ◽  
Nhat-Nam Le-Dong ◽  
Valérie Cuthbert ◽  
Stéphane Denison ◽  
David Gozal ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pilot Study ◽  
Sleep Bruxism ◽  
Intelligence Analysis ◽  
Accurate Detection ◽  
Mandibular Movements

scholarly journals 0792 Mandibular Movement Monitoring with Artificial Intelligence Analysis for the Diagnosis of Sleep Bruxism

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A301-A302
Author(s):  
J Martinot ◽  
N Le-Dong ◽  
V Cuthbert ◽  
S Denison ◽  
D Gozal ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cluster Analysis ◽  
Sleep Bruxism ◽  
Specific Pattern ◽  
Measurement Unit ◽  
Type I ◽  
Negative Consequences ◽  
Mandibular Movement ◽  
Artificial Intelligence Technology ◽  
Mandibular Movements

Abstract Introduction Sleep bruxism (BXM) is the result of rhythmic muscular masticatory activity (RMMA) and can be captured by masseters surface electromyography (sEMG). Despite the multiple adverse negative consequences of BXM, a simple reliable home diagnostic device is currently unavailable, with in laboratory audio-video polysomnography (type I PSG) remaining the gold standard diagnostic tool. Mandibular movements (MM) recordings during sleep can readily identify RMMA, are simple to set up and can be easily repeated from night to night. Here, we aimed to identify stereotypical MM in patients with BXM, and to develop RMMA automatic detection and BXM diagnosis using an artificial intelligence-based approach. Methods MM were recorded by a dedicated sensor (Sunrise, Namur, Belgium) in 12 patients with BXM during type I PSG. The Sunrise system consists of a coin-sized hardware that is comfortably placed on the subject’s chin. Its embedded inertial measurement unit communicates via Bluetooth with a smartphone and automatically transfers MM signals to a cloud-based infrastructure at the end of the night. Data processing and analysis are then performed in Python programming language. A time series cluster analysis was applied to sequences of masseters sEMG and MM signals during BXM episodes (n=300) and during spontaneous micro-arousals (n=300). Then, a convolutional neuronal network (CNN) was developed to identify BXM and distinguish it from spontaneous micro-arousals while exclusively relying on MM signal. Results Based on the cluster analysis, BXM periods were characterized by a specific pattern of MM signals (higher frequency and amplitude), which was closely associated with the sEMG signals but clearly differed from the MM signal patterns during micro-arousals. CNN-based classifier distinguished the BXM events from other RMMAs during micro-arousals and respiratory efforts with an overall accuracy of 91%. Conclusion Sleep bruxism can be automatically identified, quantified, and characterized with mandibular movements analysis supported by artificial intelligence technology. Support This work was supported by the French National Research Agency (ANR-12-TECS-0010), in the framework of the “Investissements d’avenir” program (ANR-15-IDEX-02). https://life.univ-grenoble-alpes.fr.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Phasic jaw motor episodes in healthy subjects with or without clinical signs and symptoms of sleep bruxism: a pilot study

2013 ◽  
Vol 18 (1) ◽  
pp. 187-193 ◽  
Author(s):  
Shuichiro Yoshizawa ◽  
Takeshi Suganuma ◽  
Masayuki Takaba ◽  
Yasuhiro Ono ◽  
Takuro Sakai ◽  
...  
Keyword(s):  
Pilot Study ◽  
Healthy Subjects ◽  
Clinical Signs ◽  
Signs And Symptoms ◽  
Sleep Bruxism ◽  
Clinical Signs And Symptoms

An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Hongbin Cai ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Intelligence Analysis

Effect of an occlusal splint on sleep bruxism in children in a pilot study with a short-term follow up

2013 ◽  
Vol 17 (4) ◽  
pp. 418-422 ◽  
Author(s):  
Lilian Chrystiane Giannasi ◽  
Israel Reis Santos ◽  
Thays Almeida Alfaya ◽  
Sandra Kalil Bussadori ◽  
Luis Vicente Franco de Oliveira
Keyword(s):  
Pilot Study ◽  
Sleep Bruxism ◽  
Occlusal Splint ◽  
Short Term

scholarly journals Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 155
Author(s):  
Joaquim Carreras ◽  
Naoya Nakamura ◽  
Rifat Hamoudi
Keyword(s):  
Artificial Intelligence ◽  
Overall Survival ◽  
B Cell ◽  
Mantle Cell Lymphoma ◽  
Cell Lymphoma ◽  
High Accuracy ◽  
Gene Set Enrichment Analysis ◽  
Intelligence Analysis ◽  
Mantle Cell ◽  
Pan Cancer

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

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